Real?time monitoring and timely diagnosis of transmission line faults are the prerequisite for the safe operation of transmission lines. Due to the complex shooting environment of transmission line images, individual detection or segmentation can not meet the real?time requirements, and it is difficult to extract small parts and occluded parts in the picture. In order to more accurately locate the target position, detect and segment small parts and occluded parts in the picture, an improved fully convolutional instance?aware semantic segmentation （FCIS） simultaneous detection and segmentation method for transmission line components is proposed. This method introduces the idea of region of interest (ROI) Align algorithm into the FCIS model, and proposes position sensitive inside/outside?region of interest (PS2?ROI) Align, which uses bilinear interpolation method to effectively solve the problem that the ROI in the input image feature map does not match the position information in the original image. And the gradient backpropagation algorithm is used to solve the problem of poor detection and segmentation accuracy due to the difficulty in extracting the features of small fittings and occluded fittings in the image. The detection and segmentation experiment was carried out on the transmission line detection and segmentation data set of this structure. The results showed that the small targets that could not be detected and segmented in the modified figure had indicators and masked detection segmentation. Compared with other detection models, the FCIS model has the highest mean average precision (mAP), which is 1.73% higher than before improvement.